DESC0024802

Super-resolution and single-molecule imaging allows for the ability to measure (i.e., resolve) objects smaller than the ~200 nm resolution limit of visible light. This resolving power allows scientists to probe more deeply into cellular metabolism and subcellular compartments, potentially leading to new advances in renewable energy production from plant and microbial sources. However, most super-resolution microscopy platforms are operated manually and require significant investment of time and highly skilled labor to image and analyze data, making it a bottleneck in otherwise high-throughput experimental and screening pipelines. Additionally, manual imaging introduces subjectivity and variation, reducing reproducibility. This SBIR/STTR Phase I project aims to create an automated version of a novel light sheet- based microscope system for super-resolution/single-molecule imaging. This unique imaging modality increases the longevity of live-cell imaging beyond traditional limits and enables users to perform super- resolution imaging deeper than traditional limits in cells. The proposed Phase I work first involves redesigning the optical path of the system to be compatible with a multiwell plate format and engineering and validation of a prototype system. Additionally, the alignment of the light sheet for each sample/well will be automated, and a machine learning-based neural network for single molecule detection will be developed, which will form the basis of an automated data analysis pipeline. The end result of this DOE project will be a system that can automatically detect, focus, and image single molecules across multiple live or fixed samples in multiwell plates, followed by automated image analysis and data export. Integration with liquid and plate handling robotics will enable seamless integration into high-throughput workflows. The customizable, user-friendly, plug-and-play platform will be compatible with existing inverted microscopes, increasing the accessibility of single molecule and super-resolution imaging to a wider range of users, while also increasing the data content of imaging workflows. This will open up new avenues for discovery and innovation by helping researchers identify individual species, tissues, organelles, or biological and structural components in an image and discover the physical conditions, spatial/temporal relationships, physical connections, and chemical exchanges that facilitate the flow of information and materials among organisms or biological components.